DeepParcellation: fast and accurate fast and accurate brain MRI parcellation by deep learning
Project description
DeepParcellation Package
DeepParcellation: fast and accurate brain MRI parcellation by deep learning
Contributions
- The project was initiated by Dr. Lim (abysslover) and Dr. Choi (yooha1003).
- The code is written by Dr. Lim at Gwangju Alzheimer's & Related Dementias (GARD) Cohort Research Center (GARD CRC), Chosun University.
- This research was conducted in collaborations with the following people: Eun-Cheon Lim1, Uk-Su Choi1, Yul-Wan Sung2, Gun-Ho Lee1 and Jungsoo Gim1.
- Gwangju Alzheimer's & Related Dementias (GARD) Cohort Research Center, Chosun University, Gwangju, Republic of Korea
- Department of Brain Imaging, Tohoku University, Sendai, Miyagi, Japan
- The manuscript will be available in the future.
Getting Started
A step-by-step tutorial is provided in the following sections.
Prerequisites
You should install CUDA-enabled GPU cards with at least 8GB GPU memory manufactured by nVidia, e.g., Titan XP.
Prepare T1-weighted MR images
- Convert MR images to Neuroimaging Informatics Technology Initiative (NIfTI) format.
- The parent directory name of a NIfTI file path will be used as Subject Id during prediction.
- You can specify either an input path of the NIfTI file or input direcotry of many NIfTI files.
Install DeepParcellation
- Install Anaconda
- Download an Anaconda distribution: Link
- Create a Conda environment
conda create -n deepparc
- Install DeepParcellation in Linux
conda activate deepparc
pip install deepparcellation
- Run DeepParcellation
conda activate deepparc
deepparcellation -o=/tmp/test --i=./subject-0-0000/test.nii.gz
or
deepparcellation -o=/tmp/test --i=./subject-0-0000/
NOTE:
- You must always activate the conda enviroment before running DeepParcellation if you opened a new console.
Contact
Please contact abysslover@gmail.com if you have any questions about DeepParcellation.
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deepparcellation-1.0.0.tar.gz
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